Multi-Use Learning Instance for Optimized Image Retrieval

Abstract

Dear Editor, Retrieving target images accurately shows more and more prominent significance in the era of digital media and big data. Although there are many classic methods proposed, the overwhelming majority of them are still improved based on the strategy of machine learning. In recent years, deep learning models (such as convolutional neural networks [1]–[3], restricted Boltzmann machines [4], [5], autoencoders [6]–[8], and sparse coding [9], [10]) have used more complicated networks to extract essential features more completely. Moreover, the overwhelming advantages of experimental results support it to replace the traditional machine learning methods in a short while. On the basis of classic models, many innovative models [11], [12] have been proposed, demonstrating better practical application value. Although we must admit that deep learning models have provided revolutionary changes, the huge computing resource consumption is also a burden that can not be underestimated. Even if some methods can reduce the amount of learning instances relatively, they are at the cost of accuracy reduction in most cases, and even some models have obvious limitations which are only effective for some categories.